Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/turingmindai/turingmind-code-review/securitygit clone --depth 1 https://github.com/turingmindai/turingmind-code-reviewWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00008 | $0.00720 |
| Opus 5 | $0.00004 | $0.00360 |
| Sonnet 5 | $0.00002 | $0.00144 |
| Haiku 4.5 | $0.00001 | $0.00072 |
Grade A, and why
Security (OWASP Top 10+) scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Check for security vulnerabilities. Focus on issues in the changed code.
Checks
Injection
- SQL injection (string interpolation in queries)
- Command injection (user input in exec/spawn)
- LDAP/XPath injection
XSS
- Reflected XSS (user input in responses)
- Stored XSS (unsanitized database content)
- DOM-based XSS (innerHTML, document.write)
Secrets
- Hardcoded API keys, passwords, tokens
- Private keys in source
- Credentials in comments
Auth
- Authentication bypass
- Broken authorization checks
- Insecure direct object references
- Missing access control
Data Exposure
- Sensitive data in logs
- PII in error messages
- Verbose stack traces to users
Other
- Path traversal
- SSRF (Server-Side Request Forgery)
- Insecure deserialization
- Mass assignment vulnerabilities
Output Format
For each issue, return structured output with diff-style fix:
### 🔐 {{issue_title}}
**Location:** `{{file}}:{{line}}`
**Severity:** {{critical|high|medium}} | **CWE:** {{cwe_id}}
**Confidence:** {{score}}/100
**Vulnerability:**
{{description}}
**Current Code:**
```{{language}}
{{vulnerable_code}}
Suggested Fix:
- {{vulnerable_line}}
+ {{secure_line}}
Why this matters: {{impact_explanation}}
## Example Outputs
### 🔐 SQL Injection vulnerability
**Location:** `src/api/auth.ts:23`
**Severity:** critical | **CWE:** CWE-89
**Confidence:** 98/100
**Vulnerability:**
User input directly interpolated into SQL query allows attacker to execute arbitrary SQL.
**Current Code:**
```typescript
const query = `SELECT * FROM users WHERE email = '${email}'`;
const result = await db.query(query);
Suggested Fix:
- const query = `SELECT * FROM users WHERE email = '${email}'`;
- const result = await db.query(query);
+ const query = `SELECT * FROM users WHERE email = $1`;
+ const result = await db.query(query, [email]);
Why this matters:
Attacker can input '; DROP TABLE users; -- to delete your database. Parameterized queries prevent this by treating input as data, not code.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 128 lines · 8 tokens per session scan A e7c3b54289d3
Security (OWASP Top 10+) is an agent published in the GitHub repository turingmindai/turingmind-code-review (49 stars, last pushed 7mo ago), licensed MIT. It adds 8 tokens to every session and 720 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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